System and method for determining relative preferences for marketing, financial, internet, and other commercial applications

ABSTRACT

A method transforms ratings data for evaluation items to response data that indicates an approach, if any, toward the evaluation items and avoidance from the items. Approach entropy values and avoid entropy values may be generated for the items based on the response data. A relative preference order for the items may be determined from the generated approach and avoid entropy values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of application Ser. No. 13/584,155,filed Aug. 13, 2012, for a SYSTEM AND METHOD FOR DETERMINING RELATIVEPREFERENCES FOR MARKETING, FINANCIAL, INTERNET, AND OTHER COMMERCIALAPPLICATIONS, which is a continuation of application Ser. No.12/172,914, filed Jul. 14, 2008, for a SYSTEM AND METHOD FOR DETERMININGRELATIVE PREFERENCES FOR MARKETING, FINANCIAL, INTERNET, AND OTHERCOMMERCIAL APPLICATIONS, now U.S. Pat. No. 8,255,267, which claims thebenefit of U.S. Provisional Application Ser. No. 60/959,352, filed Jul.13, 2007 for a SYSTEM AND METHOD FOR APPLYING PREFERENCE DYNAMICS TOMARKETING, FINANCIAL, COMMERCIAL, HUMAN RESOURCES, ECONOMICS, INTERNETPROCESSES, AND OTHER APPLICATIONS, and U.S. Provisional Application Ser.No. 60/959,406, filed Jul. 13, 2007 for a SYSTEM AND METHOD FOR APPLYINGPREFERENCE DYNAMICS TO SECURITY AND OTHER USES, which applications arehereby incorporated by reference in their entireties.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to systems and methods for determiningpreferences with regard to marketing and other stimuli.

Background Information

In developing or selecting a new product or service, a company, such asa consumer product company, typically undertakes a number of marketresearch studies to evaluate the new product or service. These studiesmay include surveys, interviews and focus groups. Focus groups are oftenused to acquire feedback and other information regarding new products orservices. Focus groups allow the consumer product company to test thenew product and make changes before it is made available to the public.The feedback and other information generated by the focus group provideinsight into the potential acceptance of the new products or services inthe marketplace. Despite their use, focus groups have been subject tocriticism. In particular, it has been noted that members of focus groupsoften try to please the moderator rather than offer independent opinionsor evaluations. In addition, the feedback and other information can bemisinterpreted.

In addition to market research studies, new tools related toneuromarketing have been used to study consumer's sensorimotor,cognitive, and affective response to marketing stimuli. Withneuromarketing, researchers use diagnostic or other equipment, such asfunctional magnetic resonance imaging (fMRI) to measure changes inactivity in parts of the consumer's brain, and sensors to measurechanges in a consumer's physiological state, such as heart rate,respiratory rate, etc., in an effort to learn why consumers makeparticular decisions. Although, the brain does not “lie”, measures madefrom the brain depend on very sophisticated technology, and cannot bereadily implemented on the Internet. Furthermore, neuromarketingapproaches have yet to find any recurrent, robust, and scalable law-likepatterns to human judgment and decision-making (i.e, choice behavior).Absent applications based on such recurrent, robust, and scalablelaw-like patterns based on behavior, physiological, or neural signals,neuromarketing approaches, and traditional approaches based on focusgroups, cannot accurately and objectively map out the space of humanpreference.

SUMMARY OF THE INVENTION

In an embodiment, a method analyzes preferences of participants. Eachparticipant may be presented with a plurality of evaluation items thatillustrate various marketing options. Response data from theparticipants that indicates at least an approach, if any, toward theevaluation items is obtained. An approach entropy value is generated andstored for the marketing options based on the response data for eachparticipant. A relative preference order for each participant of themarketing options may be determined from the generated approach entropyvalues graphed against the mean intensity of these responses.

In another embodiment, a computer-readable medium includes instructionsexecutable by a data processing entity. The medium includes instructionsto present to one or more participants a plurality of evaluation itemsthat illustrate various marketing options. The medium further includesinstructions to obtain response data from the participants that isindicative of an approach toward the evaluation items and an avoidanceof the evaluation items. The medium also includes instructions togenerate for each participant an approach entropy value and/or an avoidentropy value for the marketing options based on the response data. Themedium further includes instructions to display, or print or otherwiserecord and/or present a plot of the approach entropy values for themarketing options as a function of the avoid entropy values for themarketing options. The medium may further include instructions todisplay or print for the individual, and for the group of participants,graphs of approach and avoid entropy values, graphs of mean responseintensity versus approach/avoid entropy values, graphs of mean responseintensity versus standard deviation, and graphs of other relevantlocation plus dispersion estimates.

In another embodiment, a method analyzes preferences of participants.Each participant may be presented with a plurality of evaluation itemsthat illustrate various marketing options. Response data from theparticipants that indicates at least an approach and/or avoidance towardthe evaluation items by the participants is obtained. An approach and/oravoid standard deviation value is generated and stored for the marketingoptions based on the obtained response data. A plot of the approachstandard deviation values for the marketing options as a function of theresponse data indicative of at least the approach toward the evaluationitems is displayed, printed or otherwise recorded and/or presented. Aplot of the avoid standard deviation values for the marketing options asa function of the response data indicative of at least the avoidancetoward evaluation items may be displayed or printed. An indication ofthe ease or difficulty in deciding to approach the marketing options isdetermined based on the plot, and may be used to determine theeffectiveness of the marketing options.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention description below refers to the accompanying drawings, ofwhich:

FIG. 1 is a schematic illustration of a system in accordance with anembodiment of the invention;

FIG. 2 is functional diagram of a relative preference server;

FIGS. 3 and 7 are flow diagrams of preferred methods in accordance withembodiments of the invention;

FIG. 4 is an illustration of a display screen used in the collection ofresponse data;

FIGS. 5 and 22 are illustrations of timelines for the presentation ofevaluation items;

FIG. 6 is a schematic illustration of a data record; and

FIGS. 8-21 are plots of relative preferences data.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

Overview

As described herein, relative preferences can be assessed by keypressprocedures that quantify (i) decision-making regarding approach,avoidance, indifference, and uncertain/inconsistent responses, and (ii)judgments that determine the magnitude of approach and avoidance. Overthe course of multiple experiments, the inventor evaluated whethersplitting ratings of preference into explicit measures of approach andavoidance (while viewing beautiful and average faces, or distinctcategories of facial expression, or distinct categories of physicalactivity, or food while the viewer is in different hedonic deficitstates) reveals any regular patterns in behavior, such as a trade-off inapproach and avoidance, or recurrent lawful patterns as observed withKahneman and Tversky's prospect theory, or the Herrnstein-Baum matchinglaw. Patterns for approach and avoidance were discovered by the inventorthat are (i) recurrent across all stimulus types, and (ii) robust tonoise. These patterns included: (a) a preference trade-off thatcounterbalances approach and avoidance responses, (b) a value functionlinking preference intensity to uncertainty about preference, and (c) asaturation function linking preference intensity to its standarddeviation. All patterns demonstrated symmetry between group andindividual data. In addition, the keypress-based value function had thesame mathematical structure as the value function in prospect theory,and was consistent with the matching law for individual data. Theinventor further evaluated the specificity of these patterns to genderbiases and clinical abnormalities. These patterns verified known biasesbetween females and males toward beautiful and average faces. When usedto evaluate cocaine dependent subjects versus healthy controls, thesepatterns quantified the phenotype of the restricted behavioralrepertoire associated with addiction. In general, these patternsprovided a basis for mapping the space of relative preference in groupsor individuals, leading to the current application of the uses of theserecurrent, robust, and scalable patterns in relative preference forcommercial applications.

In accordance with the present invention, the underlying data(keypresses) is transformed in accordance with one or more definedmathematical procedures for presentation to the analyst who will makedecisions based on the transformed data. As discussed in detail below,these procedures include, but are not limited to, a Shannon Entropytransformation, a Value Function transformation, and a Saturationtransformation. I have found that, over a wide range of subjects andtests, the responses of test subjects strongly tend to cluster alongfunctional data paths defined by these transformations, reflecting anunderlying pattern of human behavior and choices that is not readilyobservable when the data is presented in raw format (e.g., simpletabulations of key presses). This enables the analyst to more readilyand confidently assess the responses and quickly differentiate the moredesirable from the lesser. It also enables the analyst to quicklyrecognize responses that deviate substantially from the establishedpatterns and thus are to be considered suspect.

Relative Preference System

FIG. 1 is a schematic illustration of a relative preference system 100in accordance with an embodiment of the invention. The system 100includes a relative preference server 200 coupled to a managementconsole 102 via a communication link 104. Server 200 is also coupled toa data communication network, such as the Internet, as illustrated byInternet cloud 106, via a communication link 107. Coupled to, or partof, the Internet 106 are a plurality of participant consoles, such asconsoles 108 a-d. Server 200, management console 102 and participantconsoles 108 a-d may communicate by exchanging discrete packets orframes through the data communication network according to predefinedcommunication protocols, such as the Transmission ControlProtocol/Internet Protocol (TCP/IP) or the Internetwork Packet eXchange(IPX) protocol, among others.

In an embodiment, the management console 102 and the participantconsoles 108 are each computers, such as workstations, desktops,notebooks, laptops, palm-tops, smart phones, personal digital assistants(PDAs), etc. Accordingly, the management console 102 and the participantconsoles 108 each include one or more input devices, such as a keyboard,mouse, microphone, etc., one or more output devices, such as a display,speakers, etc., and communication facilities. Suitable computers for useas the management console 104 and the participant consoles 108 includethe HP Pavilion series of computers from Hewlett Packard Co. of PaloAlto, Calif., the Inspiron series of computers from Dell Inc. of RoundRock, Tex., and the MacBook series of computers from Apple, Inc. ofCupertino, Calif. Those skilled in the art will recognize that othercomputer platforms may be advantageously utilized with the presentinvention.

It should be understood that in other embodiments, one or more or evenall of the participant consoles 108 may be directly connected to therelative preference server 200.

Relative Preference Server

FIG. 2 is a schematic illustration of the relative preference server200. Server 200 includes a communication facility 202, at least onekeypress procedure application 204, a keypress data manipulation engine206, and a keypress data store 208. The keypress procedure application204, the keypress data manipulation engine 206, and the keypress datastore 208 are each coupled to the communication facility 202. Thekeypress procedure application 204 may include a plurality of evaluationitems, such as evaluation item no. 1, evaluation item no. 2, etc.,designated generally 210. The keypress procedure application 204 mayalso include a data collector component 211. The keypress datamanipulation engine 206 may include one or more plotting functions, suchas plotting function 212, and one or more envelope/curve fittingcomponents, such as envelope/curve fitting component 214. The keypressdata store 208 may include a plurality of response data records, such asrecord 600, and a plurality of relative preference data records, such asrecord 216.

The communication facility 202 may include one or more softwarelibraries for implementing a communication protocol stack allowingserver 200 to exchange messages with other entities of the system 100(FIG. 1), such as the management console 102 and the participantconsoles 108 a-d. The communication facility 202 may, for example,include software layers corresponding to the Transmission ControlProtocol/Internet Protocol (TCP/IP), although other communicationprotocols, such as Asynchronous Transfer Mode (ATM) cells, the InternetPacket Exchange (IPX) protocol, the AppleTalk protocol, the DECNetprotocol and/or NetBIOS Extended User Interface (NetBEUI), among others,could be utilized. Communication facility 202 further includestransmitting and receiving circuitry and components, including one ormore network interface cards (NICs) that establish one or more ports,such as wired or wireless ports, for exchanging data packets and frameswith other entities of the system 100.

Server 200 may be a computer server having one or more processors, suchas a central processing unit (CPU), and memories, such as a hard diskdrive, interconnected by a system bus. Suitable servers for use with theinvention include the HP ProLiant series of servers from Hewlett PackardCo., the PowerEdge series of servers from Dell Inc., and the IBM BladeCenter series of servers from International Business Machines Corp. ofArmonk, N.Y., among others.

It should be understood that one or more of the components of therelative preference server 200 may alternatively or additionally beincluded within the management console 102. For example, each of thecomponents of the relative preference server 200 may be included in themanagement console 102, thereby eliminating the need for a separateserver 200.

The keypress procedure application 204 and the keypress datamanipulation engine 212 may include or comprise programmed orprogrammable processing elements containing program instructions, suchas software programs, modules, or libraries, pertaining to the methodsand functions described herein, and executable by the processingelements. Other computer readable media may also be used to store andexecute the program instructions. The keypress procedure application 204and the keypress data manipulation engine 212 may also be implemented inhardware through a plurality of registers and combinational logicconfigured to produce sequential logic circuits and cooperating statemachines. Those skilled in the art will recognize that variouscombinations of hardware and software components, including firmware,also may be utilized to implement the invention.

The keypress data store 208 may be implemented on a hard disk drive, aredundant array of independent disks (RAID), a flash memory, or othermemory.

Marketing Options

FIG. 3 is a flow diagram of a method 300 according to an embodiment ofthe invention. A developer identifies a set of marketing options(experimental conditions) to be tested or evaluated, and creates ordefines a corresponding set of evaluation items (stimuli) for each ofthe marketing options, as indicated at block 302. Each evaluation itemmay illustrate a different view or use of the marketing option. In anembodiment, a set of marketing options may be proposed for existingproducts, packaging, or services, or advertising or marketing campaigns,etc. Those skilled in the art will recognize that other marketingoptions may be used, such as items in an inventory.

For example, suppose a consumer product company has developed five newproposed products or packaging, such as new razor blades, new packagingalternatives for shampoo, new soft drinks, new containers for a softdrink, etc., and is trying to choose which of the new proposed productsor packaging designs to release to the marketplace. Each of these fiveproposed products or packaging designs represents a marketing option.For each marketing option, the developer creates or defines a set ofevaluation items that can be sensed or perceived, e.g., visually,aurally, tactilely or through taste or smell, or some combinationthereof, by participants. For example, for the proposed razor blades orthe proposed soft drink containers, the set of evaluation items may be aseries of photographs or video clips of each proposed razor blade orsoft drink container. That is, for proposed razor blade no. 1, thedeveloper may define or create 20 different photos of razor blade no. 1,such as the razor blade itself, someone using the razor blade, etc. Forproposed razor blade no. 2, the developer may define or create 20different photos of razor blade no. 2, and so on, so that for eachmarketing option there is a set of evaluation items. In an embodiment,each evaluation item illustrates only one marketing option.

It should be understood that the evaluation items may take other formsbesides photographs or video clips. For example, if the marketingoptions for which relative preferences data is being sought are songs,then the evaluation items may be different excerpts from the songs thatcan be played through the speakers of the participant consoles 108. Ifthe marketing options are perfumes or other scented products, theevaluation items may be samples of the perfumes or scents that theparticipant can smell.

Keypress Procedure

The developer next develops a keypress procedure incorporating the setsof evaluation items for the marketing options, as indicated at block304. In an embodiment, a suitable keypress procedure is implementedthrough a computer program or application that displays the photographsor video clips to each participant, and allows the participant to eitherextend or shorten the time that a given photograph or video clip isdisplayed by entering keypresses on a keyboard at the participantconsole. The term “keypress procedure” is intended to broadly define anyprocedure in which preference based response data is generated byparticipants in response to being presented with evaluation items. Asdescribed herein, other response data besides keypress data may begenerated by the participants and utilized by the system and method ofthe invention.

FIG. 4 is a schematic illustration of a screen 400 of a participantconsole 108 (FIG. 1) displaying the visible portions of a keypressprocedure presented to a participant, and FIG. 5 is a timeline 500 of akeypress procedure. The screen 400 includes a viewing area 402 in whichthe current evaluation item, e.g., the current photograph or video clip,is presented or displayed. The screen 400 also may include a timeremaining icon 404, which provides a visual indication to theparticipant of how much longer the currently presented evaluation item,e.g., photograph or video clip, will continue to be displayed. Withreference to the timeline 500, the portion of the keypress procedureassociated with each evaluation item, e.g., each given photograph orvideo clip, has a start time 502. In a first phase 504, the currentevaluation item, e.g., the current photograph or video clip, may bedisplayed in viewing area 402 (FIG. 4) for approximately 200milliseconds (ms). In a second phase 506, the current evaluation item,e.g., the current photograph or video clip, is removed from the viewingarea 402 leaving the viewing area blank for approximately 1.8 seconds(s). In a third phase 508, the current evaluation item, e.g., thecurrent photograph or video clip, is once again displayed in the viewingarea 402.

During the third phase 508, the participant can act to either lengthenor shorten the time that the current evaluation item, e.g., the currentphotograph or video clip, continues to be displayed in the viewing area402. If the participant takes no action, the current evaluation item,e.g., the current photograph or video clip, is removed or stopped at adefault time 510, which may be eight seconds, and the keypress procedureproceeds to the next evaluation item, e.g., the next photograph or videoclip. If the participant finds the current evaluation item to bedesirable or appealing, the participant may lengthen the time by whichit remains displayed past the default time 510 by alternatingly pressingtwo keys on the keyboard of the participant console, referred to as the“approach” keys, such as the keys corresponding to the numbers 7 and 9,in a toggle-like fashion. By continuing to toggle between the twoapproach keys, the participant can cause the current evaluation item,e.g., the current photograph or video clip, to continue to be displayedup to a maximum time 512, e.g., fourteen seconds, thereby signaling botha preference toward the current evaluation item and the intensity of theparticipant's preference toward the current evaluation item.

If the participant dislikes the current evaluation item, the participantmay shorten the time during which it is displayed by alternatinglypressing two other keys of the keyboard, referred to as the “avoidance”keys, such as the keys corresponding to the numbers 1 and 3, in atoggle-like fashion. By continuing to toggle between the two avoid keys,the participant can stop the display of the current evaluation item,e.g., the current photograph or video clip, sooner than the default time510, thereby signaling both a dislike of the current evaluation item andthe intensity of the participant's dislike toward the current evaluationitem.

It should be understood that a participant may utilize both the approachkeys and the avoid keys to variable degrees in an alternating fashion,while being presented with an evaluation item, e.g., while viewing agiven photograph or video clip, thereby signaling both preference anddislike, e.g., uncertainty, regarding the current evaluation item.

Thus, the response data generated by a participant may indicateindifference or ambivalence toward the evaluation item (no action by theparticipant), a preference toward the evaluation item (toggling of justthe approach keys), an avoidance of the evaluation item (toggling ofjust the avoid keys), or uncertainty/inconsistency in preferenceregarding the evaluation item (toggling both the approach and the avoidkeys).

The time remaining icon 404 indicates how much longer the currentevaluation item, e.g., the current photograph or video clip, will bedisplayed. The time remaining icon 404 may be a stack of thin horizontallines that may be on, e.g., colored green, or off, similar to a graphicvolume indicator. Those skilled in the art will understand that othergraphical elements or widgets may be used. If the participant takes noaction, the time remaining icon 404 begins dropping at the start of thethird phase 508 and is completely empty at the default time 510, atwhich point the current evaluation item, e.g., the current photograph orvideo clip, is removed from the viewing area 402, and the keypressprocedure application 204 proceeds with the next evaluation item, e.g.,the next photograph or video clip. If the participant toggles theapproach keys, then the time remaining icon 404 drops at a slower rateand may not reach an empty point until sometime after the default time510 up to a maximum at the end time 512, depending on how many timesand/or how quickly the participant presses the approach keys. If theparticipant toggles the avoid keys, then the time remaining icon 404drops at a fast rate and may reach an empty point before the defaulttime 510, depending on how many times and/or how quickly the participantpresses the avoid keys.

In an embodiment, the keypress procedure presents each evaluation item,e.g., each photograph or video clip, to the participant according to theabove-described process, as illustrated by the timeline 500. In anotherembodiment, there may be a maximum total test time for the entirekeypress procedure. If a participant reaches this maximum total testtime before viewing all of the evaluation items, the keypress procedureends and the participant is not presented with or exposed to theremaining evaluation items.

In an embodiment, each marketing option or experimental condition haseight or more evaluation items, and may have on the order of twenty ormore evaluation items. Nonetheless, those skilled in the art willunderstand that other numbers of marketing options and/or evaluationitems may be used. For example, a keypress procedure having on the orderof twenty marketing options or experimental conditions each having threeevaluation items may be created.

The developer in addition to selecting the evaluation items alsodetermines the sequence or order in which the evaluation items arepresented to each participant. In an embodiment, the evaluation items ofthe various marketing options are interspersed following conservativeexperimental psychology procedures so that one experimental stimulus orresponse does not overweight the effects of others. This may be done bycounterbalancing all categories of items, one item forward and one itembackward in a sequence of such items. It may also be performed bypseudo-random intermixture of experimental stimuli with jitter of theinter-stimulus intervals so that the items, modeled by a hemodynamicwaveform (as may be done for single-trial functional magnetic resonanceimaging studies), produce minimal carryover effects by simulation.

Suitable keypress procedures are also described in I. Aharon et al.Beautiful Faces Have Variable Reward Value: fMRI and BehavioralEvidence, Neuron Vol. 32, pp. 537-551 (November 2001), and M. Strauss etal. fMRI of Sensitization to Angry Faces, Neuroimage, pp. 389-413 (April2005), which are hereby incorporated by reference in their entireties.

It should be understood that the keypress procedure does not have be atoggle-like pressing of two keys by two fingers. For example, theprocedure could involve a series of mouse clicks, a triple button pressactivated by three fingers in a row, a repetitive typewriter keystroke,etc.

It should further be understood, as indicated above, that othertechniques or procedures may be used instead of a keypress procedure.Other such procedures may involve a lever press, a potentiometer, anon/off switch, or a touch screen element, among others.

With the lever-press procedure, the whole hand or a finger or foot oreye saccade or other motor output of the participant may be used torepetitively signal his or her preference toward approaching, avoiding,doing nothing about, or variably approaching/avoiding the evaluationitem or stimulus. Such a procedure may be advantageous for participantswhose fine-motor coordination is not well developed, or where physicalconstraints are imposed by the data collection process, the environment,or the personal medical condition of the participant.

The potentiometer procedure may be implemented using a button that theparticipant twists, e.g., to move a cursor on the screen in order to setthe cursor at a level of the experience or effort that the participantis willing to expend. Alternatively, it may be implemented as thescrolling of a mouse, or as a lever or joystick that the participantpushes in any of N directions to signal N types of action. It may alsobe implemented with a device to scroll the participant's response asrepresented by an increasing or decreasing bar on the side of thescreen.

The on/off switch procedure may advantageously be used with sound basedevaluation items or stimuli, such as songs, or with any temporallyextended type of stimuli, in which an evaluation item starts for a setamount of time, and the participant can terminate the exposure at anytime, or repeat it. For example, the participant can start and stop theevaluation item, e.g., a song, a picture, a scent or odor, a physicalsensation, etc., at any time with one type of signal, or that will stopon its own when it reaches a pre-determined exposure time or “defaulttime”, unless the participant produces another type of signal so thatthe evaluation item continues on for another pre-determined window oftime. With enough repetitions of the repeat signal, the evaluation item,e.g., song or film, may be heard or viewed by the participant.

The response data of the on/off switch procedure may be a view time orexposure time for each evaluation item. This response data may bepartitioned as “avoidance” if it is below a mean view time for the groupof participants, or as “approach” if it is above the mean view time.Alternately, the view time or exposure time response data may be used toproduce a positive value function plot and saturation plot alone fromanalyses.

As described, a suitable procedure may permit a participant to controlthe amount of his or her exposure to a visual, auditory, somatosensory,gustatory, olfactory, vestibular, or other stimulus or evaluation item.Each procedure involves some way to transcribe physical effort(involving energy expenditure by the participant) into time of exposure.

In another embodiment, the procedure also may be used to signal how muchmoney a participant would spend to approach, avoid, do nothing about, orvariably approach/avoid an evaluation item or stimulus. Alternately, a“keypress” procedure may be used to signal a transaction using somemeasure other than money, such as points, or any item of commercialvalue that could be used for barter.

Those skilled in the art will understand that other procedures may beused or that modifications to the procedures described herein may bemade.

Keypress Data Collection

A plurality of participants run the keypress procedure, as indicated atblock 306 (FIG. 3). In an embodiment, the keypress procedure application204 including the evaluation items is stored at and accessible from therelative preference server 200 (FIG. 1). A participant located at arespective participant console, e.g., console 108 a, accesses thekeypress procedure application 204 from the server 200, utilizing thedata communication network, e.g., the Internet 106. For example, theparticipant may access the keypress procedure application 204 and runthe keypress procedure through a World Wide Web (WWW) web site hosted bythe server 200. The participant may be given a login identity (ID) thatis unique to the particular participant, and a password to access thekeypress procedure application 204 and run the keypress procedure, orthey may not need login and password procedures.

It should be understood that the participant may be provided withinstructions on how to run the keypress procedure.

It should be further understood that each participant may providedemographic information about himself or herself, such as age, sex,marital status, employment status, income, education level, buyinghabits, computer Internet Protocol (IP) address, race, languages spoken,etc.

In an embodiment, a participant may download a keypress procedure fromserver 200, run it on his or her console 108, and transmit responsedata, e.g., by e-mail, to server 200. Those skilled in the art willrecognize that other ways of accessing and running a keypress procedureand collecting response data may be used.

Response data generated during each participant's running of thekeypress procedure is captured and stored, as indicated at block 310.The data collector component 211 of the keypress procedure application204 captures and stores the response data, which may include the totaltime that each evaluation item is maintained, e.g., viewed forphotographs or video clips, by the participant, the number of approachkeypresses and the number of avoid keypresses. The data collector 211may organize the response data into records, and store the records atthe keypress data store 208.

FIG. 6 is a schematic illustration of a response data record 600 for agiven participant. The data record 600 is organized into a plurality offields, including a start field 602, a participant ID field 604, and aevaluation item area for each evaluation item in the keypress procedure,such as evaluation item areas 606, 608 and 610, which correspond toevaluation items 1, 2 and N. The participant ID field may store theparticipant's name or login ID. Each evaluation item area, moreover, mayinclude a item ID field 612 that identifies the particular evaluationitem, a total time field 614 that holds the total time that therespective evaluation item was viewed by the participant, an approachkeypresses field 616 that stores the number of approach keypressesentered by the participant for that evaluation item, and an avoidkeypresses field 618 that stores the number of avoid keypresses enteredby the participant for that evaluation item. The data record 600 mayalso include an end field 620. For each participant running the keypressprocedure, a respective response data record 600 is created and storedat the keypress data store 208.

It should be understood that additional or other response data may becollected.

In an embodiment, the keypress procedure is defined so that, for eachmarketing option or experimental condition, there will be evaluationitems that received approach keypresses and other evaluation items thatreceived avoidance keypresses by each participant. For example, supposethe experimental conditions are faces that may be categorized as:beautiful female, average female, beautiful male, and average male.Suppose further that, for each experimental condition, there are twentyevaluation items, e.g., twenty pictures of beautiful female faces. Inthis case, a participant may enter approach keypresses for 18 of the 20beautiful female faces, but avoidance keypresses for the other two.Furthermore, the keypress procedure may be defined in such a way thatthe participant while being presented with a current evaluation itemassociated with a given marketing option or experimental condition isunlikely to remember how he or she responded to prior evaluation itemsassociated with this given marketing option or experimental condition.

Relative Preferences Data Processing

After each participant runs the keypress procedure, and the resultingresponse data is collected and stored at the keypress data store 208,the response data is processed to generate relative preference data forthe marketing options represented by the evaluation items, as indicatedat block 310. Specifically, the keypress data manipulation engine 206accesses the response data records 600 stored at the keypress data store208, and processes the information stored in those records 600 togenerate relative preference data. As described herein, the relativepreference data generated from the response data may include one or moreentropy values, mean approach keypress, mean avoid keypresses, andstandard deviation values for approach and avoidance keypresses, amongothers.

Shannon Entropy

In an embodiment, the keypress data manipulation engine 206 computes,for each participant, an approach Shannon entropy value (H₊) and anavoid Shannon entropy value (H⁻) for each marketing option. The approachShannon entropy value (H₊) may be computed as follows:

$H_{+} = {\sum\limits_{i = 1}^{N}{p_{i}*{\log \left( {1/p_{+ i}} \right)}}}$

where,

i is the current evaluation item,

N is the total number of evaluation items for a given marketing option,

p_(+i) is the relative approach probability for the i^(th) evaluationitem, and

the log function is to base 2.

The relative approach probability for the i^(th) evaluation itemcorresponding to a given marketing option may be computed as follows:

$p_{+ i} = \frac{m_{+ i}}{M}$

where,

m_(+i) is the number of approach keypresses for i^(th) evaluation item,and

M is the total number of approach keypresses for all evaluation itemscorresponding to the same marketing option.

It should be understood that view time (or other response data) may beused instead of approach keypresses.

The avoidance Shannon entropy value (H⁻) similarly may be computed asfollows:

$H_{-} = {\sum\limits_{i = 1}^{N}{p_{- i}*{\log \left( {1/p_{- i}} \right)}}}$

where,

i is the current evaluation item,

N is the total number of evaluation items for a given marketing option,

p_(−i) is the relative avoid probability for the i^(th) evaluation item,

and the log function is to base 2.

The relative avoid probability for the i^(th) evaluation itemcorresponding to a given marketing option may be computed as follows:

$p_{- i} = \frac{l_{+ i}}{L}$

where,

l_(−i) is the number of avoid keypresses for i^(th) evaluation item, and

L is the total number of avoid keypresses for all evaluation itemscorresponding to the same marketing option.

FIG. 7 is a flow diagram of a method of computing relative preferencedata. The keypress data manipulation engine 206 first may determine arelative approach probability for each evaluation item per participant,as indicated at block 702. The keypress data manipulation engine 206 maydetermine a relative avoid probability value for each evaluation item,as indicated at block 704. Continuing with the above example, suppose aparticipant entered a total of 400 approach keypresses while viewing the20 photographs or video clips for proposed razor blade 1. Supposefurther that the participant entered the following number of approachkeypresses for the first three photographs or video clips of proposedrazor blade 1:

photo/video clip #1: 9 approach keypresses

photo/video clip #2: 15 approach keypresses

photo/video clip #3: 12 approach keypresses

The keypress data manipulation engine 206 may compute the relativeapproach probability associated with these three photographs orvideoclips as follows:

p1=9/400=0.0225

p2=15/400=0.0375

p3=12/400=0.03

Using the computed relative approach probability values, an approachShannon entropy value (H₊) may be computed for each marketing option foreach participant, as indicated at block 706. A mean approach intensityvalue for each marketing option also may be computed. Furthermore, usingthe computed relative avoid probability values, an avoid Shannon entropyvalue (H⁻) may be computed for each marketing option for eachparticipant, as indicated at block 708. A mean avoid intensity value foreach marketing option also may be computed. The approach Shannon entropyvalue (H₊) and the avoidance Shannon entropy value (H⁻) computed for aparticipant may be as follows:

razor blade 1: {3.1, 2.2}

razor blade 2: {0.5, 5.1}

razor blade 3: {4.2, 1.3}

razor blade 4: {1.9, 4.4}

It should be understood that other techniques or equations may beemployed to compute the approach and avoid Shannon entropy values orother entropy values. For example, another way of computing suitableapproach and avoid entropy values is given by:

$H_{+} = {\sum\limits_{i = 1}^{N}{{p_{+ i}/\log}\; p_{+ i}}}$$H_{-} = {\sum\limits_{i = 1}^{N}{{p_{- i}/\log}\; p_{- i}}}$

It should be understood that the keypress data manipulation engine 206may be configured to compute only an approach Shannon entropy value, oronly an avoid Shannon entropy value for each marketing option.

It also should be understood that the keypress data manipulation engine206 may be configured to compute other entropy values, such as entropyvalues based on second or third order models. A suitable equation forcomputing entropy of a second order model is given by:

$H = {\sum\limits_{i = 1}^{m}{p_{i}{\sum\limits_{j = 1}^{m}{P_{ji}\log \; P_{ji}}}}}$

where P_(ij) is the conditional probability that the present item is thej^(th) item in the set given that the previous item is the i^(th) item.

A suitable equation for computing entropy of a third order model isgiven by:

$H = {\sum\limits_{i = 1}^{m}{p_{i}{\sum\limits_{j = 1}^{m}{{Pji}{\sum\limits_{j = 1}^{m}{P_{kji}\log \; P_{kji}}}}}}}$

where P_(kji) is the conditional probability that the present item isthe k^(th) item in the set given that the previous item is the j^(th)item and the one before that is the i^(th) item.

Standard Deviation

In an embodiment, the keypress data manipulation engine 206 also maycompute an approach standard deviation value for each marketing optionper participant, as indicated at block 710, and an avoid standarddeviation value for each marketing option per participant, as indicatedat block 712.

The approach standard deviation value may be computed as follows.

$\sigma_{+} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {K_{i} - K_{M}} \right)^{2}}}$

where

σ₊ is the approach standard deviation,

N is the total number of evaluation items for the subject marketingoption,

K_(i) is the number of approach keypresses for the i^(th) evaluationitem, and

K_(M) is the mean number of approach keypresses for all of theevaluation items for the subject marketing option.

That is, to compute the approach standard deviation, the keypress datamanipulation engine 206 computes the mean approach keypresses for all ofthe evaluation items for a given marketing option, K_(M). The keypressdata manipulation engine 206 computes the deviation of the approachkeypresses for each evaluation item from the mean, and calculates thesquare of these deviations (K_(i)−K_(M))². The keypress datamanipulation engine 206 then calculates the mean of the squareddeviations, and take the square root of the mean of the squareddeviations.

The avoid standard deviation, σ⁻, may be calculated in a similar manner.

Signal to Noise (SNR)

In an embodiment, the keypress data manipulation engine 206 is furtherconfigured to compute an approach signal to noise (SNR+) value, asindicated at block 714, and an avoid signal to noise (SNR−) value, asindicated at block 716. A suitable equation for computing SNR+ is givenby:

SNR₊=mean approach keypress intensity/σ₊

Similarly, a suitable equation for computing SNR− is given by:

SNR=mean avoid keypress intensity/σ⁻

CoVariance

In an embodiment, the keypress data manipulation engine 206 is furtherconfigured to compute an approach covariance (CoV₊) value, as indicatedat block 718, and an avoid covariance (CoV⁻) value, as indicated atblock 720. Suitable equations for computing CoV₊ and CoV⁻ are given by:

CoV+=1/SNR₊

CoV−=1/SNR⁻

Thus, for each marketing option, the keypress data manipulation engine206 may generate the following relative preference data per participant,along with other location and dispersion measures of relevance to his orher preference behavior:

{H+, H−, mean approach keypress, mean avoid keypress, σ₊, σ⁻, SNR₊,SNR⁻, CoV₊, CoV⁻}

It should also be understood that pre-existing data may be utilized asthe response data. For example, suppose a consumer product company orother entity already has a series of consumer rankings of items, such asbooks or movies, on a scale from 1-5, with 5 indicating a consumer'spreference toward the item and 1 indicating a consumer's dislike of theitem. In this case, the rankings could be converted as shown below:

Rank Keypress Equivalent 1 20 avoid keypresses 2 10 avoid keypresses 3no keypresses 4 10 approach keypresses 5 20 approach keypresses

It should be understood that other conversions of preexisting product orservice rankings to response data could be applied. In this way, storesof preexisting rankings of products or services could be used tocalculate relative preference data for subsequent analysis, as describedherein.

Relative Preference Data Plotting and Analysis

The relative preference data may be analyzed in order to make judgmentsand decisions regarding the marketing options that were evaluated orreviewed by the participants. Specifically, the relative preferencesdata may be plotted and the plots printed, displayed or otherwisepresented to an evaluator, as indicated at block 312 (FIG. 3).Specifically, an evaluator may command the plotting function 212 of thekeypress data manipulation engine 206 to generate one or more plots fordisplay on the management console 102 and/or for printing. In anembodiment, the plots may include one or more of a Trade-off plot, aValue Function plot and a Saturation plot.

In addition, the plots and/or the relative preference data may beanalyzed to derive an outcome or select an action, as indicated at block314. These decisions may include, among other things, selecting one ormore of the proposed products, services or product packaging for releaseto the marketplace, selecting one or more of the proposed advertising ormarketing programs, targeting one or more proposed products or servicesto a particular target audience or sub-market. Those skilled in the artwill understand that other decisions may be made based on the relativepreferences data.

The keypress data manipulation engine 206 may be further configured tosearch the relative preference data for patterns and/or to organize therelative preference data in certain ways, and to present identifiedpatterns to the evaluator to facilitate the selection of an outcome oraction.

Preference Trade-Off Plot

FIG. 8 is an illustration of a Trade-off plot 800 of relativepreferences data computed for a single participant. The Trade-off plot800 includes an x-axis 802 and a y-axis 804 that intersect at origin805. The x-axis 802 represents H⁻ values while the y-axis 804 representsH₊ values. As indicated above, for each marketing option, an {H+, H−}value pair may be computed. Accordingly, assuming the participant ranthe keypress procedure for four marketing options, the {H+, H−} valuepair 806 a-d computed for each marketing option is plotted in theTrade-off Plot 800. Research by the inventor has demonstrated that the{H+, H−} value pairs of individuals and groups typically, but notalways, fall generally along an arc 808 of constant radius, r, from theorigin 805. This arc 808, moreover, provides an indication of therelative preference ordering of the four marketing options by theparticipant. Specifically, the marketing options that appear toward theupper left of the plot 800, i.e., marketing options 3 and 1, which havehigh H₊ values, were preferred by this participant while the marketingoptions that appear toward the lower right portion of the plot 800,i.e., marketing options 4 and 2, which have high H⁻ values, weredisliked by this participant.

The curve fitting component 214 may be directed to find a best-fitcurve, such as arc 808, through the {H+, H−} value pairs 806.

In addition to plotting the {H+, H−} value pairs for a singleparticipant, the evaluator may command the plotting function 212 to plotthe {H+, H−} value pairs for all of the participants on a singleTrade-off plot. By reviewing such a Trade-off plot, the evaluator mayascertain a preference for a particular marketing option by a majorityof the participants, a dislike of a particular marketing option by amajority of the participants, etc. This interpretation may be quantifiedby determining the center of mass for the {H+, H−} value pairs for eachmarketing option or experimental condition, and comparing between thesecenters of mass for each marketing option or experimental condition.Alternately, the quantification of differences between marketing optionsor experimental conditions may be performed by evaluating radial andangular distribution plots, as described below, and showing asegregation of distributions between experimental conditions.

Alternately, it may be shown by application of bucket statistics, whichare used in voxel-based neuroimaging analyses, such as statisticalparametric mapping. This technique may be applied to the preferencetrade-off plots, and these graphs can be pixilated in the radial andpolar dimensions. The incidence of real and hypothetical subjectpresence in each bucket or pixel can be compared to a Gaussiandistribution, in a t-statistic analysis. The t-value can then beconverted into a pseudocolor map on the preference trade-off plot,quantifying the segregation of experimental data between any two or moreexperimental conditions.

The keypress data manipulation engine 206 may be further configured toperform these tasks.

The evaluator may also direct the keypress data manipulation engine 206to determine the number of participants that ranked the four marketingoptions in the same relative preference order. Suppose thisdetermination produces the following relative preferences data:

Relative No. of % of Total Preference Order Participants Participants 3,1, 4, 2 96 48 1, 3, 2, 4 56 28 2, 1, 4, 3 22 11 3, 2, 4, 1 16 8 3, 4, 1,2 10 5

A review of this relative preferences data by the evaluator reveals that152 or 76% of the participants preferred marketing options 3 and 1 outof the four marketing options, and of these 152 participants most ofthem preferred marketing option 3 over marketing option 1. As a result,a decision may be made to release the proposed product or service thatcorresponds to marketing options 1 and 3 to the marketplace.

Radial and Angular Distribution Plots.

With reference to FIG. 8, each {H+, H−} value pair also has polarcoordinates, e.g., {r, θ}, where r is the radial distance from theorigin of the Trade-off Plot 800 to the respective {H+, H−} value pair,and θ is the angle of the radial, r, from the x-axis 802. Consideringentropy pair 806 d, for example, there is a radius, r₄, 812 and a polarangle, θ₄, 814. Thus, for every participant, in addition to a {H+, H−}value pair for each marketing option, there is also a polar coordinatepair for each marketing option.

In an embodiment, the keypress data manipulation engine 206 isconfigured to derive the polar coordinates, e.g., {r, θ}, for eachmarketing option for all of the participants. The plotting function 212of the keypress data manipulation engine 206 is configured to produce aradial distribution plot and/or an angular distribution plot for displayat the management console 102 and/or for printing.

FIG. 9 is an illustration of a radial distribution plot 900, which hasan x-axis 902 and a y-axis 904 that intersect at an origin 906. Thex-axis 902 represents the radial distance, r, of the {H+, H−} valuepairs for all of the participants. The y-axis 904 indicates the numberof participants, or other frequency information related to theparticipants. Based on research by the inventor, the radial distributionplot typically takes the form of curve 908. Curve 908, moreover, mayhave a full width half maximum measure (W) 910, or another dispersionmeasure which can be tested with the Levene statistic for differences invariance. The size of W 910 of curve 908 provides the evaluator with anindication of how restrictive the range of relative preference is, for agroup of participants toward the marketing option represented by thecurve 908. A narrowed spectra, as demonstrated by a low W measure, showsthat these participants have less variance in their responses and thusgreater certainty in their choice behavior. A wide spectra, asdemonstrated by a high W measure, shows that these participants have areduced certainty in their choices.

FIG. 10 is an illustration of an angular distribution plot 1000, whichhas an x-axis 1002 and a y-axis 1004 that intersect at an origin 1006.The x-axis represents the angle, θ, of the {H+, H−} value pairs for allof the participants. The y-axis 1004 indicates the number ofparticipants. The angular distribution plot 1000 may show a separatecurve, e.g., curves 1008 a-d, for each of the marketing options. Thatis, curve 1008 a may correspond to marketing option 2, curve 1008 b maycorrespond to marketing option 4, curve 1008 c may correspond tomarketing option 1, and curve 1008 d may correspond to marketing option3. The closer a curve is to θ=90 degrees, e.g., curves 1008 and 1008 d,the higher the approach entropy for the respective marketing option.Thus, the marketing options represented by curves near θ=90 were foundby the participants to be desirable. Similarly, the closer a curve is toθ=0 degrees, the higher the avoid entropy for the respective marketingoption. Thus, the marketing options represented by curves near θ=0 weredisliked by the participants.

FIG. 13 is an exemplary Trade-off plot 1300 for a plurality ofparticipants for four marketing options. As with plot 800, Trade-offplot 1300 has an x-axis 1302 that represents avoid entropy, H⁻, and ay-axis 1304 that represents approach entropy H₊ that intersect at origin1305. Depending on the noise characteristics of the experimental set-up,the relative preference data of Trade-off plot 1300, moreover, may havea central tendency that may be approximated by an arc 1306 of constantradius from the origin 1305.

The Trade-off plot, also referred to as a “preference trade-off”,represents a manifold across many subjects which can have a centraltendency characterized by radius r=√{square root over ((H₊ ²+H⁻ ²))}.The manifold generally has an internal border characterized by thesimulation of a participant only making one of two decisions—to approachor to avoid. It has an outside border characterized by range-matchedGaussian noise simulated from hypothetical participants who makeresponses and are each matched to one real participant in the cohort forthe range of responses. Many individual participants will produceresponses across a set of experimental stimuli that fall clustered alongthe radius r=√{square root over ((H₊ ²+H⁻ ²))} line; but this may not benecessarily so.

In an embodiment, the “preference trade-off” plot is evaluated orconsidered in light of the “value function” plot and the “saturation”plot, as described below.

It should be understood that one or more preference Trade-off plots maybe generated based on other relative preference data besides Shannonentropy. For example, the plotting function 212 may be configured togenerate an SNR Trade-off plot. FIG. 16 is an illustration of an SNRTrade-Off plot 1600. The SNR Trade-off plot 1600 includes an x-axis 1602and a y-axis 1604 that intersect at origin 1606. The x-axis 1602represents SNR⁻ values while the y-axis 1604 represents SNR₊ values. Asindicated above, for each marketing option, an {SNR₊, SNR⁻} value pairmay be computed. These {SNR₊, SNR⁻} value pairs, e.g., value pairs 1608a-d, are plotted in the SNR Trade-off plot 1600. The envelope/curvefitting component 214 may be configured and/or directed to derive aboundary envelope 1610 for the relative preference data presented in theSNR Trade-off plot 1600.

The plotting function 212 may be further configured to generate a CoVTrade-off plot. FIG. 17 is an illustration of a CoV Trade-off plot 1700.The CoV Trade-off plot 1700 includes an x-axis 1702 and a y-axis 1704that intersect at origin 1706. The x-axis 1702 represents CoV⁻ valueswhile the y-axis 1704 represents CoV₊ values. As indicated above, foreach marketing option, a {Cov₊, CoV⁻} value pair may be computed. These{Cov₊, CoV⁻} value pairs, e.g., value pairs 1708 a-e, are plotted in theCoV Trade-off plot 1700. The envelope/curve fitting component 214 may beconfigured and/or directed to fit a curve, e.g., curve 1710, to therelative preference data contained in the CoV Trade-off plot 1700.

Value Function Plot

FIG. 11 is an illustration of a Value Function plot 1100 for therelative preference data generated for a single participant. The ValueFunction plot 1100 includes an x-axis 1102 and a y-axis 1104 thatintersect at origin 1105. The x-axis 1102 represents mean keypresseswith the positive side of the x-axis 1102 representing mean approachkeypresses and the negative side of the x-axis 1102 representing meanavoid keypresses. The y-axis 1104 of the value function plot 1100represents the Shannon entropy, with the positive side of the y-axis1104 representing H₊ and the negative side of the y-axis 1104representing H⁻.

As indicated above, for each marketing option, there is a {H+, meanapproach keypress} value pair and a {H−, mean avoid keypress} valuepair. For each marketing option, these two value pairs are plotted onthe Value Function Plot 1100, as indicated at 1106 a-h. That is, foreach marketing option, e.g., marketing option 1, there are two pointsthat are plotted, one point, e.g., 1106 a, in an H₊/mean approachkeypress quadrant 1108 of the value function plot 1100, and the otherpoint, e.g., 1106 e, in a H⁻/mean avoid keypress quadrant 1110.

The order in which the data points 1106 a-h for the marketing optionsappear on the Value Function Plot 1100 provides an indication of theparticipant's relative ordering of the marketing options. Specifically,a participant's preference toward a marketing option increases in orderof increasing H₊ values, and the participant's dislike of a marketingoption increases in order of increasing H⁻ values. The participant whoserelative preference data appears in the value function plot 1100 rankedthe four marketing options in the following relative order in terms ofapproach from high to low: 3, 1, 4, 2. The participant also ranked thefour marketing options in the following order in terms of avoid fromstrongly avoid to weakly avoid: 2, 4, 1, 3. As shown, for thisparticipant, the relative order of the marketing options in the avoidquadrant 1110 of the Value Function plot 1100 is symmetrical to therelative order of the marketing options in terms of approach. It shouldbe understood that this may not always be the case.

Indeed, the relative ordering of preferences across the viewedmaterials, e.g., evaluation items and/or marketing options, may bedifferent between the positive and negative keypress portions of thegraph. This difference can be considered an indication ofuncertainty/inconsistency connected to preference decisions andjudgments. The differences in the relative orderings between thepositive and negative components of the value function plot can bequantified by a Wilcoxin test of rank order. Strong inconsistencies inrank ordering of relative preferences for approach and avoidanceresponses may be associated with a trade-off plot where {H+, H−} valuepairs are plotted far from the central tendency of the group manifold,and do not obviously convey rank ordering of relative preferences. Whereconsistency does exist in the value function graph between approach andavoidance responses for one or more experimental conditions, relativepreference can be interpreted for that subset of experimental conditionsin that participant or subgroup of participants. For all participants,it may be important to assess the difference in slopes between theapproach and avoidance sections of the value function plot, to determinehow “loss averse” a participant or a subgroup of participants isregarding the marketing options or experimental conditions tested. Theextent of loss aversion may segregate subgroups of participants andsuggest a marketing strategy toward one set of consumers that emphasizeshow a product or a strategy promoting a product reduces some aspect ofloss or bad outcome. The difference in slopes between approach andavoidance components of the value function plot is one part of howparameter fitting information for the graphs of participants can beuseful. Other features of the parameter fits to the value functions ofindividuals include that related to the intercept of the x-axis, whichreflects the core transaction costs that a participant sees around anyconsumatory, defensive, or procreative activity.

The curve fitting component 214 may evaluate the data 1106 a-d plottedin the H₊/mean approach keypress quadrant 1108 of the value functionplot 1100 to determine an approach boundary envelope 1112. Research bythe inventor has shown that the approach boundary envelope 1112 mayfollow a power function given by:

f(x)=ax ^(b) +c

where a, b, and c are variables, or it may be approximated by alogarithmic function given by:

f(x)=a*log_(B) [b(x+c)]+d

where a, b, c and d are variables, and B is the base of the logarithm.

The curve fitting component 214 may also evaluate the data 1106 e-hplotted in the H⁻/mean avoid keypress quadrant 1110 of the valuefunction plot 1100 to determine an avoid boundary envelope 1114.Research by the inventor has shown that the avoid boundary envelope 1114may follow a power function given by:

f(x)=ax ^(b) +c

where a, b and c are variables, or it may be approximated by alogarithmic function given by:

f(x)=a*log_(B) [b(x+c)]+d

where a, b, c, and d are variables and B is the base of the logarithm.

It should be understood that a single Value Function Plot 1100 may begenerated using the preference data for all of the participants that ranthe keypress procedure. Similarly, separate Value Function Plots 1100may be generated for those participants who had the same order ofmarketing options in terms of approach, avoidance or both.

FIG. 14 is an exemplary Value Function plot 1400 for a plurality ofparticipants for four marketing options. As with plot 1100, the ValueFunction plot 1400 has an x-axis 1402 and a y-axis 1404 that intersectat origin 1405. The x-axis 1402 represents mean keypresses with thepositive side of the x-axis 1402 representing mean approach keypressesand the negative side of the x-axis 1402 representing mean avoidkeypresses. The y-axis 1404 represents the Shannon entropy, with thepositive side of the y-axis 1404 representing H₊ and the negative sideof the y-axis 1404 representing H⁻.

The relative preference data within the approach entropy (H₊)/approachkeypress portion of the Value Function plot 1400 follows an approachboundary envelope 1406. As shown in FIG. 14, the approach boundaryenvelope 1406 may fit or conform to a power function, e.g.,H₊=1*(k−5)^(0.334)−0.5. Similarly, the relative preference data withinthe avoid entropy (H⁻)/avoid keypress portion of the Value Function plot1400 follows an avoid boundary envelope 1408. As shown in the figure,the avoid boundary envelope 1408 may fit or conform to a power function,e.g., H⁻=−1*(−k)^(0.45).

The approach and avoid boundary envelopes 1406, 1408 may also fit orconform to logarithmic functions.

The “value function plot” is either an envelope for group data, or afunction for individual data. In both of these scenarios, it can bemodeled as a logarithm, or as a power function. This means that theH₊/mean approach keypress plot and H⁻/mean avoidance keypress plot areboth considered as a logarithm, or as a power function. Given thatalteration of the x-axis into logarithmic coordinates produces anenvelope (group data) or function (individual data) that becomes linear,the envelope or function could be considered to be a power law. Thisargues more strongly for the power function formulation of both theH+/mean approach keypress plot and H−/mean avoidance keypress plot.Another argument for using the power function formulation of the valuefunction graph, is that the “saturation function” is best fit as anenvelope (group data) or function (individual data) when it incorporatesa power formulation.

It should be understood that one or more Value Function plots may begenerated based on other relative preference data besides Shannonentropy. For example, the plotting function 212 may be configured togenerate one or more SNR Value Function plots. FIG. 18 is anillustration of an SNR+ Value Function plot 1800. The SNR+ ValueFunction plot 1800 has an x-axis 1802 and a y-axis 1804 that intersectat origin 1806. The x-axis 1802 represents mean approach keypressintensity (K+) values while the y-axis 1804 represents SNR+ values. Asindicated above, the relative preference data includes a {SNR+, K+}value pair for each of the marketing options. These {SNR+, K+} valuepairs, e.g., value pairs 1808 a-e, are plotted in the SNR+ ValueFunction plot 1800. The envelope/curve fitting component 214 may beconfigured and/or directed to determine an envelope 1810 for therelative preference data contained in the SNR+ Value Function plot 1800.

FIG. 19 is an illustration of an SNR− Value Function plot 1900. The SNR−Value Function plot 1900 has an x-axis 1902 and a y-axis 1904 thatintersect at origin 1906. The x-axis 1902 represents mean avoid keypressintensity (K−) values while the y-axis 1904 represents SNR− values. Asindicated above, the relative preference data includes a {SNR−, K−}value pair for each of the marketing options. These {SNR−, K−} valuepairs, e.g., value pairs 1908 a-e, are plotted in the SNR− ValueFunction plot 1900. The envelope/curve fitting component 214 may beconfigured and/or directed to determine an envelope 1910 for therelative preference data contained in the SNR− Value Function plot 1900.

The plotting function 212 may be further configured to generate one ormore CoV Value Function plots. FIG. 20 is an illustration of a CoV+Value Function plot 2000. The CoV+ Value Function plot 2000 has anx-axis 2002 and a y-axis 2004 that intersect at origin 2006. The x-axis2002 represents mean approach keypress intensity (K+) values while they-axis 2004 represents CoV+ values. As indicated above, the relativepreference data includes a {CoV+, K+} value pair for each of themarketing options. These {CoV+, K+} value pairs, e.g., value pairs 2008a-e, are plotted in the CoV+ Value Function plot 2000. Theenvelope/curve fitting component 214 may be configured and/or directedto determine an envelope 2010 for the relative preference data containedin the SNR− Value Function plot 2000.

FIG. 21 is an illustration of a CoV− Value Function plot 2100. The CoV−Value Function plot 2100 has an x-axis 2102 and a y-axis 2104 thatintersect at origin 2106. The x-axis 2102 represents mean avoid keypressintensity (K−) values while the y-axis 2104 represents CoV− values. Asindicated above, the relative preference data includes a {CoV−, K−}value pair for each of the marketing options. These {CoV−, K−} valuepairs, e.g., value pairs 2008 a-d, are plotted in the CoV− ValueFunction plot 2100. The envelope/curve fitting component 214 may beconfigured and/or directed to determine an envelope 2110 for therelative preference data contained in the SNR− Value Function plot 2100.

Saturation Plot

FIG. 12 is an illustration of a saturation plot 1200 for the relativepreference data generated by a single participant. The Saturation plot1200 has an x-axis 1202 and a y-axis 1204 that intersect at origin 1205.The x-axis 1202 represents mean keypresses with the positive side of thex-axis 1202 representing mean approach keypresses, and the negative sideof the x-axis 1202 representing mean avoid keypresses. The y-axis 1204represents the standard deviation, with the positive side of the y-axis1204 representing standard deviation for approach, and the negative sideof the y-axis 1204 representing standard deviation for avoid.

As indicated above, for each marketing option, there is a {σ₊, meanapproach keypress} value pair and a {σ⁻, mean avoid keypress} valuepair. These two value pairs are plotted on the Saturation Plot 1200, asindicated at 1206 a-d.

The distance a value pair 1206 a-d is away from the x-axis, i.e., themagnitude of the standard deviation, indicates how difficult thedecision was for the participant to either approach or avoid therespective marketing option. As indicated in the Saturation Plot 1200although the participant entered approach keypresses for both marketingoptions 1 and 3, it was a significantly easier for the participant todecide to approach marketing option 3, than marketing option 1. Incontrast, the degree of difficulty in deciding how to respond tomarketing options 2 and 4, which both received avoid keypresses, was notthat great.

It should be understood that a Saturation Plot 1200 may be generatedusing the preferences data for all of the participants that ran thekeypress procedure. Similarly, separate Saturation Plots 1200 may begenerated for those participants who had the same relative order ofmarketing options.

Based on a review of the saturation plot 1200 for a series of marketingoptions, a consumer product company may determine that, although a givenmarketing option received significant approach keypresses from theparticipants, the participants' decision to approach the given marketingoption was difficult. Accordingly, the company may choose to proceedwith a different marketing option that may have received substantiallythe same (or even slightly less) approach keypresses from theparticipants but, as reflected by the Saturation Plot 1200, theparticipants had less difficulty approaching this marketing option.Where participants had difficulty with judgment and decision-makingregarding one or more marketing options or experimental conditions, asindicated by increased standard deviation estimates relative to othermarketing options or experimental conditions, this data can then beevaluated with regard to relative loss aversion estimated from theapproach and avoidance components of the value function, and touncertainty/inconsistency with regard to differences in the relativeordering of approach and avoidance assessments for marketing options orexperimental conditions. The increased standard deviation observed withone or more marketing options or experimental conditions may be due toambivalent assessments (i.e., both high positive and high negativeassessments for items in an experimental condition, or the samecontradiction with low keypress assessments), or may be due to increasedloss aversion, making a small set of avoidance keypress responses beamplified relative to the approach keypresses. It should be understoodthat there are other ways by which the interpretations extracted fromthe standard deviation data may be integrated with features extractedfrom the value function graph and the trade-off graph.

FIG. 15 is an exemplary Saturation plot 1500 for a plurality ofparticipants for four marketing options. As with plot 1200, Saturationplot 1500 has an x-axis 1502 that represents mean keypresses, and ay-axis 1504 that represents standard deviation. The approach or positivestandard deviation values follow an approach boundary envelope 1506 thatis generally curved and leaves the baseline, achieves a maximum, andthen approaches the baseline again, in the form of a saturationfunction. Similarly, the avoid or negative standard deviation valuesfollow an avoid boundary envelope 1508 that is also curved but ofsmaller radius.

Graphs of group data for {K_(±),σ_(±)} produce distributions withwell-delineated envelopes as illustrated in FIG. 15, which will berecurrent across many different types of marketing options orexperimental conditions, and are likely to not be due to ceiling/flooreffects in the behavioral response. In exemplar graphs, {σ_(±)} reachesa maximum/minimum before moving toward the K axis, so that the intensityversus variance goes up and returns toward baseline with repetitivebehaviors, indicating a saturation relationship.

The envelope/curve fitting component 214 may be configured to determinethe boundary envelopes 1506, 1508. The fitting parameters for theenvelope are different for approach and avoidance (avoidance saturationis more compact than approach saturation), although the generaldescription of the envelope is similar.

The boundary envelopes 1506, 1508 for the Saturation plot 1500 may begiven by:

$\sigma_{+} = {{aK}_{+}^{b}{\cos \left( \frac{K_{+}}{c} \right)}}$$\sigma_{-} = {{aK}_{-}^{b}{\cos \left( \frac{K_{-}}{c} \right)}}$

where, a, b and c are variables.

In an embodiment, the plotting function 212 and the kepress datamanipulation engine 206 are configured to generate all three plots:Trade-off, Value Function, and Saturation from the generated relativepreference data. An evaluation of all three plots provides significantinformation for deciding on a course of action with regard to theevaluated marketing options. Nonetheless, it should be understood that,in other embodiments, the plotting function 212 and the keypressmanipulation engine 206 may be configured to generate only one of theTrade-off, Value Function, or Saturation plots. In still furtherembodiments, the plotting function 212 and the keypress manipulationengine 206 may be configured to generate some combination of theTrade-off, Value Function, or Saturation plots that is less than allthree plots.

As described herein, relative preference data may be analyzed orevaluated to assess (i) the relative ordering of preferences across theviewed materials, e.g., evaluation items and/or marketing options, alongthe trade-off plot, value function plot, and saturation plot, i.e., theconsistency of rank ordering across these three plots, (ii) the relativedifference in steepness of slope between curves fitted to the avoidanceand approach portions of the value function, (iii) the uncertaintyassociated with preference by the comparison of relative orderingsbetween the avoidance and approach components of the value functionplot, which may be quantified by a Wilcoxen test of rank ordering, andbetween each of these value function components and the preferencetrade-off graph, (iv) the parameter fits of the value function acrosspersons in or between groups, (v) the dispersion and characteristics ofthe radial and polar sampling of the preference trade-off, (vi) thestimuli for which subjects found preference decisions to be relatively“hard” (where the standard deviation is highest) versus “easy” (wherethe standard deviation is least). If an answer regarding relativepreference is not optimal, or unclear, moreover, these procedures can berepeated or redone with new evaluation items, experimental parametersand/or stimuli until an answer or optimal outcome is achieved.

Across the three types of graphs described, information that isextracted may be used to produce an integrated interpretation ofrelative preference for an individual, for a sub-group of individuals,and for a large group comprising distinctive sub-groups. The relativeorderings of marketing options or experimental conditions along atrade-off plot, a value function plot, or a saturation plot may belisted in rank order, as indicated at point (i) above, and may include ascalar value of the K or H value associated with their graphing so thatthe set of marketing options or experimental conditions can be describedas a vector for each participant or combined for each sub-group orgroup. Individuals may be clustered on the basis of rank orderings ofpreference or their preference vectors, and differences in preferencescan be quantified between the sub-groups using standard nonparametrictechniques for the location and dispersion across the group of the Kvalue associated with the two marketing options or experimentalconditions being compared across sub-groups. The consistency oruncertainty associated with preference may be compared betweensub-groups of people by evaluating the difference in rank ordering ofmarketing options or experimental conditions between approach andavoidance components of the graph, as indicated at point (iii) above.This uncertainty/consistency may be quantified by a Wilcoxen test ofrank ordering.

Differences in rank order of preferences and in theuncertainty/consistency of preferences may be important factors inassessing participant behavior. These differences also may be combinedwith an assessment of the ease with which participants make decisions,as indicated at point (vi) above. Rank order and consistency of rankorder between approach and avoidance do not convey the relativedifficulty of the judgment and decision-making involved with thepreference, and thus may be supplemented by an assessment of whichmarketing options or experimental conditions were associated with thelargest standard deviations. These types of information can be furthersupplemented by information regarding the relative steepness of theapproach and the avoidance value functions for the participants. Theslope of each component of the value function conveys how much aparticipant is willing to trade for a particular level of satisfactionor personal utility, as indicated at point (ii) above, related toapproach/positive and avoidance/negative goal-objects. The less steepthe slope, the more the participant is willing to trade for a particularlevel of satisfaction or personal utility. Some sets of participants mayhave strong similarities regarding their rank ordering of marketingconditions or experimental conditions, but may have significantdifferences in how much they are willing to pay for the same level ofsatisfaction. There also may be differences in terms of the transactioncosts that participants are willing to incur, which is observed by thex-intercept of the value function, and can be extracted from theparameter fits for this function, as indicated at point (iv) above.

There also may be characteristics related to how uncertainty/consistencyof rank order in the value function and the saturation function areconveyed with the preference trade-off plot. Trade-off plots may notshow distinct orderings of market options or experimental conditionsacross a set of experimental conditions, and may not fall on themanifold observed across many subjects. In such cases, one may findsignificant inconsistencies between rank ordering of approach andavoidance responses in the value function and saturation function,indicating relative preferences that are likely to be stronglyinfluenced by local factors, such as recent public discourse in the newsregarding a marketing option or experimental condition or hedonicdeficit state effects when the time scale of change associated withrelative need for an experimental condition is short, e.g., food takeson increased positive/approach assessments with hunger and is devaluedafter satiation. Some features of a trade-off plot may not be readilyapparent in the other plots, though. For instance, some participants mayshow a significant restriction in the range or dispersion of theirpreferences across the trade-off plot. Such a restriction in theirtrade-off plot may have diagnostic significance for psychiatric illness,such as addiction, or may have implications for how they are willing toNOT have a broadly distributed set of relative preferences. Suchparticipants, like investors with restricted portfolios of assets orinvestments, may be strategic in their preferences for the short term.In general, such a profile may not be very adaptive to environmentalchange or changes in local influences over the long run.

It should be understood that the invention may be implemented inconjunction with neuroimaging. For example, neuroimaging may beperformed with the advertising or marketing materials and a keypress orsimilar procedure may be implemented at relatively the same time or alater time. For example, if the keypress procedure is done outside ofthe neuroimaging, it may be used as a covariate in data analysis of thebrain imaging data. Furthermore, the results of keypress procedure andthe neuroimaging may be combined to increase the interpretive power ofthe process. Furthermore, if an optimal response is not obtained, thenthe process can be done iteratively.

It should be understood, as described above, moreover, that otherprocedures may be implemented in place of the keypress procedure. Forexample, the measure of preference in terms of keypress or time is notthe only measure by which response data may be sampled. Response data,for instance, may be sampled by an individual keypressing for units ofmoney or points that allow approach or avoidance. The units thatdemarcate relative preference do not have to be keypress or time, butcould be any medium by which trades are made between potentialgoal-objects, e.g., gold, food-items, paper money, time, ratings, etc.As described above, it is also possible to transform existing frequencydata so that it can be analyzed as described herein. For example,pre-existing movie rating data along a scale of 1-5 may be transformedan approach and avoidance scale as follows:

Rank Response Data 1 −2 2 −1 3 0 4 +1 5 +2

In this way, existing frequency data may be mapped into response data.In an embodiment, the response data may include more than approach andavoid actions.

Furthermore, the evaluation items or stimuli that are used for mappingthe preference space of an individual for marketing or advertisingpurposes need not be just stimuli related to the actual marketing oradvertising materials, but could be stimuli of more general interest,such as photographs of sports, nature, activities, hobbies, and othergeneral categories.

In addition, the present invention may be used to evaluate how relativepreference data may be altered over time by relative deficit states ordegrees of satiation, such as relative preferences for food before andafter a hunger deficit state. In this case, the evaluation items orstimuli may include both normal colored food items and discolored fooditems to make them unappetizing. Other evaluation items or stimuli mayinclude food items that are prepared and ready to eat and items that areunprepared or raw. The participants may be in one of two possible statesduring the keypress procedure: after an 18 hour fast, such as before theparticipant eats lunch, and after consuming a normal lunch. Suchevaluations may point to how the temporal delivery of marketingcommunications can be salient—some messages will induce a greaterpreference response just before normal meal times than at other times.The present invention may provide a quantification of the differences inpreference produced by these timing and stimulus alterations.

Additional Applications

It should be understood that the invention may be applied to many fieldsof endeavor. The following describes several exemplary applications ofthe present invention, but is not intended to be exhaustive. In general,applications of the invention include (a) marketing and advertising, (b)relative preference prediction to facilitate consumption based onrecommendations made by product provider, (c) optimization of searchengine functions by filtering of search results to an audiencepreference map, (d) product optimization and packaging for a targetaudience, (e) human resources, and (f) match-making, among others. Foradvising consumption, the invention may have direct implications forincreasing consumption by making recommendations to consumers, such asbook or movie recommendations. For optimization of search engineresults, the invention may have implications for the optimal placementof advertisements for viewing by search engine users. For humanresources as well as matching personnel to specific tasks, the inventionmay be applied by organizations in which a high school, college orgraduate student enters the organization with a particular career pathin mind, but may have an aptitude or preference for tasks or activitiesof the organization that are different. For match-making applications,the invention may be used to identify compatible individuals.

Movies or Literature

To evaluate movies, for example, participants or customers may be askedto complete a keypress task on the Internet. The response data may beprocessed as described herein to create a “preference vector” for theparticipant or customer in order to guide further recommendations formovies or books. The keypress procedure may be designed as an overttask, i.e., with no subliminal stimuli, and have five or more categoriesof stimuli conditions. One stimuli condition may be picture stills from20 different horror movies. A second condition may be 20 picture stillsfrom romantic movies, a third condition may be 20 picture stills fromadventure and/or action movies, a fourth condition may 20 picture stillsfrom comedies, a fifth condition may be 20 picture stills frommysteries, a sixth condition may be 20 picture stills from historicalmovies and/or documentaries, etc. In the context of literature, theexperimental conditions may represent different genres of writing andthe items in each experimental condition may include brief sections oftext or auditory recordings or readings. These pictures or other stimulicould be presented over the Internet, e.g., from a web site, to theparticipant or customer, and the keypress response data collectedregarding approach, avoidance, non-action about, or variable approachand avoidance of the evaluation items. The response data may be analyzedas described herein to assess the relative ordering of preferencesacross movie categories or literature genres on the trade-off plot(s),and assessed for which categories had the highest standard deviations,and thus represented “hard decisions” using the saturation plot, alongwith which relative orderings were consistent between approach vs.avoidance using the value function plot(s), and thus had the leastinconsistency associated with decisions for or against them.

The relative preference data then may be compared to ratings a customermade over time for various categories of movies or books to identify theextent to which “local context effects” may influence the customer'sratings. Local context effects may include the proximity of one categoryof movie or book to another in their release or publication, or thecritical reviews of particular movies or books, or the day of the weekthe movie or book was watched/read, or local events of salience. It isalso salient that other factors besides movie or book category might berelevant to a customer's keypress responses, such as the Director of themovie, leading actor or actress, or author of the book.

Security

Behavioral tasks to assess unconscious hostility toward an organizationsuch as a company, government or governmental entity, and sympathytoward violent extremism/fanaticism/intolerance may be implemented withthe present invention in a number of ways. For example, a keypressprocedure with ideologically biased pictures, e.g., pictures presentingactions supporting a government's interests or against a government'sinterests may be created or defined. This may be done either withsubliminal pictures, e.g., pictures presented fast enough that theviewer does not gain conscious recognition of what is observed, or withovert pictures, e.g., consciously observed pictures.

In the subliminal task, two sets of subliminal stimulus conditions maybe used. One security-based option or stimulus condition may includepictures showing events from a pro-terrorist and anti-governmentperspective. Another security-based option or stimulus condition mayinclude pictures that showed events from an anti-terrorist andpro-government perspective. Both sets of subliminal stimuli may bepresented before mildly positive or mildly aversive neutral pictures.The method by which the subliminal stimuli are made to be outside of theparticipant's conscious awareness may involve a number of techniques,such as the use of a “forward mask” and a “backward mask” thateffectively sandwich the very brief subliminal stimulus and act asdistracting stimuli. It should be understood that the use of masksreduces the chance that a participant may consciously perceive thesubliminal stimulus. Nonetheless, a keypress procedure forsecurity-based option may be created without masks and/or withoutsubliminal stimuli.

For example, if ten pictures are used for each category of subliminalstimulus, then a participant could complete the test session in arelatively short time frame of approximately 20*8 seconds (assuming thedefault time of the exemplar keypress task explained for marketing)=160seconds. The results of this keypress task then may be mapped into therelative preference space defined by (i) preference trade-off graphs,(ii) preference saturation graphs, and (iii) value functions ofpreference intensity against preference uncertainty. These graphs may becompared and contrasted for preference for or against violent actiontoward the subject government and its citizens. Findings of (a) activehostility toward the subject government, and (b) sympathy to extremistideology could be integrated into an algorithm to assess violentintention (IA), and incorporate other potential risks for violentbehavior, such as data from demographics, prior history, and knownassociates, to produce an index for response by governmentalauthorities.

This application may be employed at one or more points of entry into theterritory of the subject government, at its Embassies and/or consulatesoverseas, at airports, ports and other legal border crossing points, andat immigration detention sites.

FIG. 22 is a timeline 2200 of an embodiment of a keypress procedure foruse in a security-based application of the present invention. Thekeypress procedure for a security-based application may include a seriesof tasks in which both a subliminal stimulus and a corresponding overtevaluation item are presented to the participant during the course ofeach keypress task. The subliminal stimulus is presented to theparticipant for so short a time that the participant is not consciouslyaware of the subliminal stimulus. The overt evaluation item is presentedto the participant for a long enough period of time for the participantto be consciously aware of it. However, as described herein, thekeypress procedure is designed so that the participant's behaviorregarding the subliminal stimulus is reflected in his or her keypressactivity for the overt evaluation item. That is, the keypress activityentered during the presentation of the overt evaluation item is afunction of the participant's approach or avoidance regarding thesubliminal stimulus.

In an embodiment, the security-based keypress procedure includes threeexperimental conditions: (i) positive and pro-government images; (ii)neutral objects or scenes; and (iii) negative and anti-governmentimages. Items (i) and (iii), which are the subliminal stimuli, may haveextreme intensity ratings with a positive valence for (i) and a negativevalence for (iii) when rated by participants who strongly favor thegovernment, e.g., are patriotic. For the positive and pro-governmentsubliminal stimuli, the corresponding overt evaluation items may havemild positive intensity ratings and the corresponding overt evaluationitems may have mild negative intensity ratings. Suitable images for useas the overt evaluation items may be bland pictures of objects or rooms.

The portion of the keypress procedure associated with each subliminalstimulus, e.g., each pro-government or anti-government photograph orvideo clip, has a start time 2202. In a first fixation period 2204, ablank screen with a central fixation point in the form of a cross,asterisk, or other character, is presented to the participant in theviewing area 402 (FIG. 4) as a transition between the prior subliminalstimulus and the current subliminal stimulus. The first fixation period2204 may last approximately 150 milliseconds (ms). The first fixationperiod 2204 may be followed by a first forward mask period 2206 duringwhich a forward mask image is presented to the participant in theviewing area 402 of the screen 400. The first forward mask period maylast approximately 1.0 seconds (s). In an embodiment, a forward maskimage is a mosaic of image snippets from some or all of the overt pluscovert evaluation items corresponding to the current security-basedoption. The image snippets may be arranged in a checkerboard fashionwith each snippet located in a square of the checkerboard to create themosaic. Each image snippet may be small enough and the snippetsscrambled so that the forward mask image does not have any recognizableimages or patterns to the participant.

The first forward mask period 2206 may be followed by a first subliminalor covert stimulus period 2208 during which the subliminal stimulus ispresented to the participant on viewing area 402. The first subliminalstimulus period 2208 may last for 30 ms. Following the first subliminalstimulus period 2208 may be a first backward mask period 2210 duringwhich a backward mask image is presented to the participant on theviewing area 402. The first backward mask period 2210 may last forapproximately 100 ms. In an embodiment, a backward mask image is also amosaic of image snippets from some or all of the overt plus covertevaluation items corresponding to the current security-based option. Aswith the forward mask image, the image snippets for the backward maskimage may be arranged in a checkerboard fashion with each snippetlocated in a square of the checkerboard to create the mosaic. Each imagesnippet may be small enough and the snippets scrambled so that thebackward mask image does not have any recognizable images or patterns tothe participant. In an embodiment, the backward mask image is differentfrom the forward mask image.

Following the first backward mask period 2210 may be a first overtevaluation period 2212 during which the overt evaluation item that hasbeen associated with the current subliminal stimulus is presented to theparticipant in the viewing area 402. The first overt evaluation period2212 may last for 150 ms. Following the first overt evaluation itemperiod 2212 may be a second fixation period 2214 in which the viewingarea 402 is again blank with a central fixation point in the form of across or asterisk. The second fixation period 2214 may lastapproximately 1.44 seconds. Following the second fixation period 2214may be a second forward mask period 2216 in which the same forward maskimage or a new forward mask image is presented to the participant in theviewing area 402. The second forward mask period 2216 also may lastapproximately 1.0 second. The second forward mask period 2216 may befollowed by a second subliminal stimulus period 2218 during which thesubliminal stimulus is again presented to the participant in viewingarea 402. The second subliminal stimulus period 2218 also may last for30 ms. Following the second subliminal stimulus period 2218 may be asecond backward mask period 2220 during with the same backward maskimage or a new backward mask image is presented to the participant inthe viewing area 402. The second backward mask period 2220 also may lastfor approximately 100 ms. Following the second backward mask period 2220may be a second overt evaluation item period 2222. The second overtevaluation item period 2222 may last for a default time 2224, e.g.,approximately six seconds, if the participant takes no action.

As described above, the participant can act to either lengthen orshorten the time that the second overt evaluation item remains displayedin the viewing area 402. As mentioned above, if the participant takes noaction, the overt evaluation item is removed or stopped at the defaulttime 2224, which again may be six seconds, and the keypress procedureproceeds to the next subliminal stimulus/over evaluation item pair. Ifthe participant finds the overt evaluation item to be desirable orappealing, which behavior will be a function of the subliminal stimulus,the participant may lengthen the time by which it remains displayed pastthe default time 2224 by alternatingly pressing the approach keys. Bycontinuing to toggle between the approach keys, the participant cancause the overt evaluation item to continue to be displayed up to amaximum time 2226, e.g., fourteen seconds, thereby signaling both apreference toward the current evaluation item, i.e., the subliminalstimulus, and the intensity of the participant's preference toward thecurrent evaluation item, i.e., the subliminal stimulus.

If the participant dislikes the overt evaluation item, the participantmay shorten the time by which it is displayed by alternatingly pressingthe avoidance keys. By continuing to toggle between the two avoidancekeys, the participant can stop the display of the current evaluationitem sooner than the default time 2224, thereby signaling both a dislikeof the current evaluation item, i.e., the subliminal stimulus, and theintensity of the participant's dislike toward the current evaluationitem, i.e., the subliminal stimulus.

It should be understood that variations to the security-based keypressprocedure may be made, such as changing one or more time periods,re-arranging the order, adding new experimental steps in a keypresstask, and/or removing steps in the experimental task.

Internet Search Engine/Preference Vector

Behavioral tasks to assess preferences toward categories of material,such as materials used in web-based searches with a search-engine, maybe readily implemented with the present invention. This information mayalso be used to better target advertisements to search-engine users.

Specifically, an organization could ask a customer to complete akeypress task on the web, whose data is then used as a “preferencevector” to filter the output of web searches. For example, individualmay complete a keypress procedure or task with 20 distinct experimentalconditions. These experimental conditions may include the following: (1)technology, (2) religion, (3) psychology/behavior/self-help, (4)cooking/home-economics, (5) weaving/sewing/fashion, (6) animals/pets,(7) sports, (8) history/war, (9) literature, (10) art/sculpture, (11)science/math, (12) fishing/hunting/outdoors/guns, (13) cars/boating,(14) home improvement/architecture, (15) gardening/plants, (16) music,(17) economics/business, (18) politics/government, (19) lawenforcement/legal history, (20) movies/entertainment/pornography. Fromthis keypress task, the individual's trade-off graph, value function,and saturation function are produced, and they may show, for example, aclear high preference for music, above home-improvement or gardening,and above law-enforcement. This same person then may submit an Internetsearch using a search engine with the word “pick”. The word “pick” couldalso have been a phrase, or set of words. In the case of the word “pick”it has meanings related to “guitar pick”, “pick-ax”, “pick a lock”,“choose an item”, or “mistreat someone”. By utilizing the individual'spreference vector, the search engine may determine that there is ahigher probability that the reference from this particular individualwas likely to be to a “guitar pick”, than “pick-ax” (for homeimprovement or for gardening) or “pick a lock” and “mistreat someone”(for law enforcement/legal issues).

In addition, the above-described preference mapping may be used by theInternet search engine to focus the type of advertisements that aredisplayed to the individual along with the search results. Theabove-described preference mapping may also be used to select one ormore additional keypress procedures or tasks to generate more fine-tunedand specific topics and issues of interest to the individual.

Production Optimization and Packaging

Behavioral tasks to assess preferences toward variants of products, ornew products, may also be performed with the present invention. Forexample, a “keypress” procedure may be defined in which an individualbrowses music. Here, the individual may scroll through the music, ratherthan keypress. For example, the individual may have a set amount of timein which to listen to a song. The individual may end his or herlistening to a current snippet of a song with one command, or extend hisor her listening with another command once they have come to the end ofthe current music snippet. The individual also may be able to extend hisor her listening over the entire song. The collected response datarelates to the total time that the individual listened to the songgiven. Based on this response data, positive value function andsaturation plots across a number of different categories of music, oracross distinct bands/performers may be generated. In addition a meantime may be used alternately to put together a trade-off graph, apositive and negative value function and a saturation function. Fromthis relative preference mapping, the system and/or an evaluator maydetermine the types of music the individual prefers, and thus makebetter recommendations.

Similarly, a set of variants of new music that a band is producing maybe placed on a website. Based on the response data generated by manypeople and the organization of those people based on demographicinformation, relative preference data may be used to package specificsets of song versions for a new album, and to target the specificcompilations of song variants to specific consumer groups.

This same approach also may be used for competitions between bands, orto determine where musical tastes are moving in particular parts of acountry or specific target consumers.

A similar application may be implemented to select packaging for aproduct that is different than music, such as T-shirts, fashion items,etc.

Human Resources

The system and method of the present invention may be used to assessissues relating to the needs of a specific organization, such as abusiness. For example, a shipping business may need individuals formonitoring sonar, planning the course for a freight boat, determiningwhat crew are needed, matching the freight needed at a site to what isavailable for shipping at the port of origin, etc. Based on a keypressprocedure or task that assesses experimental conditions targeting thesetopics, job applicants may be more optimally placed with the job forwhich they have the highest interest.

Alternately, a keypress procedure or task may be created or defined toassess how a job applicant responds to issues of relevance for a servicecompany, or how best a service company might allocate its existing workforce. For example, cleanliness and how employees respond to having anorganized and well-maintained work environment may be important to aparticular organization or business, such as a food service company. Akeypress procedure or task may be used to assess how relevantcleanliness is to a prospective employee or an existing member of theorganization's staff.

Alternately, a detailed keypress procedure or task relating to interestsin engineering may be used in order to best select a team for aparticular contract within a technology firm.

Alternately, a keypress procedure task may be defined or created thatinvolves experimental conditions for many of the types of tasks amilitary organization, such as an army, needs in the field ofdeployment, so as to fit recruits to a needed work function.

Match-Making

For match-making, finding a match between two people may be improved bylooking for matches between two preference mappings as described abovein connection with the Internet Search Engine/Preference Vector. Mappingan individual's preference space to create a trade-off plot, valuefunction, and saturation function over some set of experimentalconditions may be referred to as a “preference map”. The relativeordering of preferences and their intensity (as from a value function)may be referred to as a “preference vector”. For match-making, thepreference vectors of various individuals may be compared to findoptimal matches by considering components of the preference maps ofdifferent people, in a step-wise manner. For example, a keypressprocedure or task may start with high-level, e.g., global experimentalconditions, and then use ever more selective sets of mappings that gointo greater detail about the likes, wants, social, cultural, andintimacy issues of participants to fine-tune matches between people.

The foregoing description has been directed to specific embodiments ofthe present invention. It will be apparent, however, that othervariations and modifications may be made to the described embodiments,with the attainment of some or all of their advantages. Therefore, it isthe object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of the invention.

What is claimed is:
 1. One or more non-transitory computer-readablemedia comprising instructions executable by one or more data processingentities, the one or more media comprising: instructions to perform akeypress procedure, the keypress procedure including presenting aplurality of evaluation items to one or more users, and obtainingresponse data from the one or more users, where the response data isgenerated based on the one or more users entering one or more inputs ata computing device as the plurality of evaluation items are beingpresented at the computing device to the one or more users, and at leasta first portion of the response data is indicative of at least anapproach toward the plurality of evaluation items by the one or moreusers; instructions to store the response data in a first memory;instructions to generate, by a processor coupled to the first memory orthe second memory, for the one or more users approach entropy values forthe plurality of evaluation items, where the approach entropy values,for the one or more users, are generated based on the stored responsedata; instructions to determine for each of the one or more users acustomized relative preference order among the plurality of evaluationitems, the customized relative preference order based on the approachentropy values generated based on the stored response data; andinstructions to present the customized relative preference order, foreach of the one or more users, on an output device, where the responsedata includes relative approach probabilities, and the approach entropyvalues are generated by applying a sum function to the relative approachprobabilities.
 2. The one or more non-transitory computer-readablemedium of claim 1 wherein the plurality of evaluation items areassociated with one or more of movies, books, and songs.
 3. The one ormore non-transitory computer-readable medium of claim 1 wherein theplurality of evaluation items are organized into a plurality ofcategories, and the response data is indicative of at least an approachtoward the plurality of categories.
 4. The one or more non-transitorycomputer-readable medium of claim 3 wherein the categories are genres.5. The one or more non-transitory computer-readable media of claim 1wherein at least a second portion of the response data is indicative ofan avoidance from the plurality of evaluation items by the one or moreusers, the one or more non-transitory computer-readable media furthercomprising: instructions to generate, by the processor, for the one ormore users avoid entropy values for the plurality of evaluation items,wherein the customized relative preference order is further determinedbased on the generated avoid entropy values.
 6. The one or morenon-transitory computer-readable medium of claim 5 wherein the responsedata that is indicative of an avoidance from the plurality of evaluationitems includes relative avoid probabilities, and the avoid entropyvalues are generated by applying a sum function to the relative avoidprobabilities.
 7. The one or more non-transitory computer-readablemedium of claim 5 further comprising: instructions to generate a plot ofthe approach entropy values versus the avoid entropy values.
 8. The oneor more non-transitory computer-readable medium of claim 1 wherein theone or more users includes a plurality of users, the medium furthercomprising: instructions to determine at least one of a number and apercentage of the plurality of users having the same customized relativepreference order among the plurality of evaluation items.
 9. (canceled)10. A method comprising: performing a keypress procedure, the keypressprocedure including: presenting a plurality of evaluation items to oneor more users, and obtaining response data from the one or more users,where the response data is generated based on the one or more usersentering one or more inputs at a computing device as the plurality ofevaluation items are being presented at the computing device to the oneor more users, and at least a first portion of the response data isindicative of at least an approach toward the plurality of evaluationitems by the one or more users; storing, by a first processor, theresponse data in a memory coupled to the first processor; generating, bythe first or a second processor, for the one or more users approachentropy values for the plurality of evaluation items, where the approachentropy values, for the one or more users, are generated based on thestored response data; determining for each of the one or more users acustomized relative preference order among the plurality of evaluationitems, the customized relative preference order based on the approachentropy values generated based on the stored response data; andpresenting, by the first, the second, or a third processor, thecustomized relative preference order, for each of the one or more users,on an output device, where the response data includes relative approachprobabilities, and the approach entropy values are generated by applyinga sum function to the relative approach probabilities.
 11. The methodclaim 10 wherein the plurality of evaluation items are associated withone or more of movies, books, and songs.
 12. The method of claim 10wherein the plurality of evaluation items are organized into a pluralityof categories, and the response data is indicative of at least anapproach toward the plurality of categories.
 13. The method of claim 12wherein the categories are genres.
 14. The method of claim 10 wherein atleast a second portion of the response data is indicative of anavoidance from the plurality of evaluation items by the one or moreusers, the method further comprising: generating for the one or moreusers avoid entropy values for the plurality of evaluation items,wherein the customized relative preference order is further determinedbased on the generated avoid entropy values.
 15. The method of claim 14wherein the response data that is indicative of an avoidance from theplurality of evaluation items includes relative avoid probabilities, andthe avoid entropy values are generated by applying a sum function to therelative avoid probabilities.
 16. The method of claim 15 furthercomprising: generating a plot of the approach entropy values versus theavoid entropy values.
 17. (canceled)
 18. The method of claim 10 whereinthe one or more users includes a plurality of users, the method furthercomprising: determining at least one of a number and a percentage of theplurality of users having the same customized relative preference orderamong the plurality of evaluation items.
 19. An apparatus comprising: anoutput device; and one or more processors, at least one of the one ormore processors coupled to the output device, the one or more processorsconfigured to: implement a keypress procedure, the keypress procedureincluding presenting a plurality of evaluation items to one or moreusers, and obtaining response data from the one or more users, where theresponse data is generated based on the one or more users entering oneor more inputs at a computing device as the plurality of evaluationitems are presented at the computing device to the one or more users,and at least a portion of the response data is indicative of at least anapproach toward the plurality of evaluation items by the one or moreusers; generate for the one or more users approach entropy values forthe plurality of evaluation items, where the approach entropy values,for the one or more users, are generated based on the response data;determine for each of the one or more users a customized relativepreference order among the plurality of evaluation items, the customizedrelative preference order based on the approach entropy values generatedbased on the stored response data; and present on the output device thecustomized relative preference order, for each of the one or more users,on an output device, where the response data includes relative approachprobabilities, and the approach entropy values are generated by applyinga sum function to the relative approach probabilities.
 20. The apparatusof claim 19 wherein the plurality of evaluation items are associatedwith one or more of movies, books, and songs.
 21. The apparatus of claim19 wherein the plurality of evaluation items are organized into aplurality of categories, and the response data is indicative of at leastan approach toward the plurality of categories.
 22. The apparatus ofclaim 21 wherein the categories are genres.
 23. The apparatus of claim19 wherein at least a second portion of the response data is indicativeof an avoidance from the plurality of evaluation items by the one ormore users, the one or more processors further configured to: generatefor the one or more users avoid entropy values for the plurality ofevaluation items, wherein the customized relative preference order isfurther determined based on the generated avoid entropy values.
 24. Theapparatus of claim 23 wherein the response data that is indicative of anavoidance from the plurality of evaluation items includes relative avoidprobabilities, and the avoid entropy values are generated by applying asum function to the relative avoid probabilities.
 25. The apparatus ofclaim 23 wherein the one or more processors is further configured to:generate a plot of the approach entropy values versus the avoid entropyvalues.
 26. The apparatus of claim 19 wherein the one or more usersincludes a plurality of users, the one or more processors furtherconfigured to: determine at least one of a number and a percentage ofthe plurality of users having the same customized relative preferenceorder among the plurality of evaluation items.
 27. (canceled)